DOI:10.35833/MPCE.2021.000546 Detection of False Data Injection Attacks on Load Frequency Control System with Renewable Energy Based on Fuzzy Logic and Neural Networks Page view: 0        Net amount: 170 Author: Ziyu Chen,Jizhong Zhu,Shenglin Li,Yun Liu,Tengyan Luo Author Affiliation: School of Electric Power Engineering, South China University of Technology, Gunagzhou 510640, China Foundation: This work was partly funded by the Science and Technology Planning Project of Guangdong Province of China (No. 2020A0505100004). Abstract: Load frequency control (LFC) system may be destroyed by false data injection attacks (FDIAs) and consequently the security of the power system will be impacted. High-efficiency FDIA detection can reduce the damage and power loss to the power system. This paper defines various typical and hybrid FDIAs, and the influence of several FDIAs with different characteristics on the multi-area LFC system is analyzed. To detect various attacks, we introduce an improved data-driven method, which consists of fuzzy logic and neural networks. Fuzzy logic has the features of high applicability, robustness, and agility, which can make full use of samples. Further, we construct the LFC system on MATLAB/Simulink platform, and systematically simulate the experiments that FDIAs affect the LFC system by tampering with measurement data. Among them, considering the large-scale penetration of renewable energy with intermittency and volatility, we generate three simulation scenarios with or without renewable energy generation. Then, the performance for detecting FDIAs of the improved method is verified by simulation data samples. Keywords: Load frequency control (LFC) ; wind turbine and photovoltaic generation ; fuzzy logic ; neural network Received:August 08, 2021               Online Time:2022/11/21 View Full Text       Download reader